Intersect Advanced Analytics and AI Platform: Empowering researchers with sustainable and adaptive access to artificial intelligence and data science expertise
Introduction: The complex nature of data-intensive research requires domain experts (e.g. ecologists) alongside skills and expertise in data science and Artificial Intelligence (AI). Intersect s Advanced Analytics and AI Platform (3AI) works closely with researchers across Australia providing hands-on support to advance research and bridge the knowledge gap to data science and AI. An example of this is a collaborative partnership as part of the Practical Ecology, Science and Technology Research Group (PEST) (NSW Department of Primary Industries, University of New England, Intersect) to develop research tools, apply for funding, and publish valuable research outputs. Methods: 3AI leverages Intersect's existing research infrastructure and provides access to a dedicated team of research-focused data scientists and AI experts via flexible full-time, part-time, and project-based arrangements. Intersect has been collaborating on a part-time basis with PEST for more than 12 months providing access to the expertise of multiple members of the 3AI team. Results: The ongoing partnership with PEST involves working collaboratively in: (1) developing cost-effective research-ready tools, (2) applying for research funding, (3) researching practical uses of AI, and (4) automating the collection and processing of data. This enables stable, sustainable, and scalable research in vertebrate pest management along with the translation to practical and impactful outcomes for the research community and society. Conclusion: Intersect has developed a platform (3AI) to bridge the gap between the knowledge of domain experts and research-relevant expertise in data science and AI to sustainably advanced research and increase the capacity and capabilities of research groups (e.g., PEST).
Institutions
- Intersect (AU)
- Intersect ENT (United States) (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2024-10-31
- DOI
- https://doi.org/10.5281/zenodo.15293365
- Primary Topic
- Genomics and Phylogenetic Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00